






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ConditionalDetrImageProcessor




class 							_lowerCAmelCase(			unittest.TestCase   ):





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase=7    ,     _lowerCamelCase=3    ,     _lowerCamelCase=3_0    ,     _lowerCamelCase=4_0_0    ,     _lowerCamelCase=True    ,     _lowerCamelCase=None    ,     _lowerCamelCase=True    ,     _lowerCamelCase=[0.5, 0.5, 0.5]    ,     _lowerCamelCase=[0.5, 0.5, 0.5]    ,     _lowerCamelCase=True    ,     _lowerCamelCase=1 / 2_5_5    ,     _lowerCamelCase=True    ,     ):
         UpperCamelCase_:   List[str]        =							size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
         UpperCamelCase_:   Union[str, Any]        =							parent
         UpperCamelCase_:   int        =							batch_size
         UpperCamelCase_:   Optional[Any]        =							num_channels
         UpperCamelCase_:   str        =							min_resolution
         UpperCamelCase_:   Union[str, Any]        =							max_resolution
         UpperCamelCase_:   str        =							do_resize
         UpperCamelCase_:   Dict        =							size
         UpperCamelCase_:   List[Any]        =							do_normalize
         UpperCamelCase_:   Union[str, Any]        =							image_mean
         UpperCamelCase_:   Tuple        =							image_std
         UpperCamelCase_:   Any        =							do_rescale
         UpperCamelCase_:   int        =							rescale_factor
         UpperCamelCase_:   List[Any]        =							do_pad





       def      _a       (   self				):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=False				):


         if not batched:
           UpperCamelCase_:   List[str]        =							image_inputs[0]
           if isinstance(_lowerCamelCase    ,     Image.Image				):
             UpperCamelCase_ ,UpperCamelCase_:   Union[str, Any]        =							image.size
           else:
             UpperCamelCase_ ,UpperCamelCase_:   List[str]        =							image.shape[1], image.shape[2]
           if w < h:
             UpperCamelCase_:   List[str]        =							int(self.size['shortest_edge'] * h / w				)
             UpperCamelCase_:   Dict        =							self.size['shortest_edge']
           elif w > h:
             UpperCamelCase_:   str        =							self.size['shortest_edge']
             UpperCamelCase_:   Dict        =							int(self.size['shortest_edge'] * w / h				)
           else:
             UpperCamelCase_:   List[str]        =							self.size['shortest_edge']
             UpperCamelCase_:   Tuple        =							self.size['shortest_edge']

         else:
           UpperCamelCase_:   List[Any]        =							[]
           for image in image_inputs:
             UpperCamelCase_ ,UpperCamelCase_:   Optional[int]        =							self.get_expected_values([image]				)
             expected_values.append((expected_height, expected_width)				)
           UpperCamelCase_:   Tuple        =							max(_lowerCamelCase    ,     key=lambda _lowerCamelCase				: item[0]				)[0]
           UpperCamelCase_:   int        =							max(_lowerCamelCase    ,     key=lambda _lowerCamelCase				: item[1]				)[1]

         return expected_height, expected_width







@require_torch
@require_vision
class 							_lowerCAmelCase(			UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       a							:						Optional[Any]										=ConditionalDetrImageProcessor if is_vision_available() else None





       def      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							ConditionalDetrImageProcessingTester(self				)





       @property
       def      _a       (   self				):
         return self.image_processor_tester.prepare_image_processor_dict()





       def      _a       (   self				):
         UpperCamelCase_:   Union[str, Any]        =							self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'image_mean'				)				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'image_std'				)				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'do_normalize'				)				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'do_resize'				)				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'size'				)				)





       def      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							self.image_processing_class.from_dict(self.image_processor_dict				)
         self.assertEqual(image_processor.size    ,     {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}				)
         self.assertEqual(image_processor.do_pad    ,     _lowerCamelCase				)

         UpperCamelCase_:   Optional[int]        =							self.image_processing_class.from_dict(
             self.image_processor_dict    ,     size=4_2    ,     max_size=8_4    ,     pad_and_return_pixel_mask=_lowerCamelCase				)
         self.assertEqual(image_processor.size    ,     {'shortest_edge': 4_2, 'longest_edge': 8_4}				)
         self.assertEqual(image_processor.do_pad    ,     _lowerCamelCase				)





       def      _a       (   self				):
         pass





       def      _a       (   self				):
         UpperCamelCase_:   int        =							self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         UpperCamelCase_:   List[Any]        =							prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=_lowerCamelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCamelCase    ,     Image.Image				)

         # Test not batched input
         UpperCamelCase_:   Optional[int]        =							image_processing(image_inputs[0]    ,     return_tensors='pt'				).pixel_values

         UpperCamelCase_ ,UpperCamelCase_:   str        =							self.image_processor_tester.get_expected_values(_lowerCamelCase				)

         self.assertEqual(
             encoded_images.shape    ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,     )

         # Test batched
         UpperCamelCase_ ,UpperCamelCase_:   str        =							self.image_processor_tester.get_expected_values(_lowerCamelCase    ,     batched=_lowerCamelCase				)

         UpperCamelCase_:   Tuple        =							image_processing(_lowerCamelCase    ,     return_tensors='pt'				).pixel_values
         self.assertEqual(
             encoded_images.shape    ,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,     )





       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         UpperCamelCase_:   Any        =							prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=_lowerCamelCase    ,     numpify=_lowerCamelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCamelCase    ,     np.ndarray				)

         # Test not batched input
         UpperCamelCase_:   Any        =							image_processing(image_inputs[0]    ,     return_tensors='pt'				).pixel_values

         UpperCamelCase_ ,UpperCamelCase_:   Dict        =							self.image_processor_tester.get_expected_values(_lowerCamelCase				)

         self.assertEqual(
             encoded_images.shape    ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,     )

         # Test batched
         UpperCamelCase_:   List[str]        =							image_processing(_lowerCamelCase    ,     return_tensors='pt'				).pixel_values

         UpperCamelCase_ ,UpperCamelCase_:   Union[str, Any]        =							self.image_processor_tester.get_expected_values(_lowerCamelCase    ,     batched=_lowerCamelCase				)

         self.assertEqual(
             encoded_images.shape    ,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,     )





       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         UpperCamelCase_:   Optional[int]        =							prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=_lowerCamelCase    ,     torchify=_lowerCamelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCamelCase    ,     torch.Tensor				)

         # Test not batched input
         UpperCamelCase_:   str        =							image_processing(image_inputs[0]    ,     return_tensors='pt'				).pixel_values

         UpperCamelCase_ ,UpperCamelCase_:   List[Any]        =							self.image_processor_tester.get_expected_values(_lowerCamelCase				)

         self.assertEqual(
             encoded_images.shape    ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,     )

         # Test batched
         UpperCamelCase_:   Tuple        =							image_processing(_lowerCamelCase    ,     return_tensors='pt'				).pixel_values

         UpperCamelCase_ ,UpperCamelCase_:   Union[str, Any]        =							self.image_processor_tester.get_expected_values(_lowerCamelCase    ,     batched=_lowerCamelCase				)

         self.assertEqual(
             encoded_images.shape    ,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,     )





       @slow
       def      _a       (   self				):
         UpperCamelCase_:   str        =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,     'r'				) as f:
           UpperCamelCase_:   List[str]        =							json.loads(f.read()				)

         UpperCamelCase_:   Optional[int]        =							{'image_id': 3_9_7_6_9, 'annotations': target}

         # encode them
         UpperCamelCase_:   int        =							ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'				)
         UpperCamelCase_:   Union[str, Any]        =							image_processing(images=_lowerCamelCase    ,     annotations=_lowerCamelCase    ,     return_tensors='pt'				)

         # verify pixel values
         UpperCamelCase_:   Any        =							torch.Size([1, 3, 8_0_0, 1_0_6_6]				)
         self.assertEqual(encoding['pixel_values'].shape    ,     _lowerCamelCase				)

         UpperCamelCase_:   Tuple        =							torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,     _lowerCamelCase    ,     atol=1e-4				)				)

         # verify area
         UpperCamelCase_:   Tuple        =							torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,     _lowerCamelCase				)				)
         # verify boxes
         UpperCamelCase_:   str        =							torch.Size([6, 4]				)
         self.assertEqual(encoding['labels'][0]['boxes'].shape    ,     _lowerCamelCase				)
         UpperCamelCase_:   Union[str, Any]        =							torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,     _lowerCamelCase    ,     atol=1e-3				)				)
         # verify image_id
         UpperCamelCase_:   Optional[int]        =							torch.tensor([3_9_7_6_9]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,     _lowerCamelCase				)				)
         # verify is_crowd
         UpperCamelCase_:   Any        =							torch.tensor([0, 0, 0, 0, 0, 0]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,     _lowerCamelCase				)				)
         # verify class_labels
         UpperCamelCase_:   Tuple        =							torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,     _lowerCamelCase				)				)
         # verify orig_size
         UpperCamelCase_:   Any        =							torch.tensor([4_8_0, 6_4_0]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,     _lowerCamelCase				)				)
         # verify size
         UpperCamelCase_:   List[str]        =							torch.tensor([8_0_0, 1_0_6_6]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,     _lowerCamelCase				)				)





       @slow
       def      _a       (   self				):
         UpperCamelCase_:   str        =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
         with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,     'r'				) as f:
           UpperCamelCase_:   int        =							json.loads(f.read()				)

         UpperCamelCase_:   List[Any]        =							{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

         UpperCamelCase_:   List[Any]        =							pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'				)

         # encode them
         UpperCamelCase_:   Any        =							ConditionalDetrImageProcessor(format='coco_panoptic'				)
         UpperCamelCase_:   int        =							image_processing(images=_lowerCamelCase    ,     annotations=_lowerCamelCase    ,     masks_path=_lowerCamelCase    ,     return_tensors='pt'				)

         # verify pixel values
         UpperCamelCase_:   List[Any]        =							torch.Size([1, 3, 8_0_0, 1_0_6_6]				)
         self.assertEqual(encoding['pixel_values'].shape    ,     _lowerCamelCase				)

         UpperCamelCase_:   Union[str, Any]        =							torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,     _lowerCamelCase    ,     atol=1e-4				)				)

         # verify area
         UpperCamelCase_:   Any        =							torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,     _lowerCamelCase				)				)
         # verify boxes
         UpperCamelCase_:   int        =							torch.Size([6, 4]				)
         self.assertEqual(encoding['labels'][0]['boxes'].shape    ,     _lowerCamelCase				)
         UpperCamelCase_:   List[str]        =							torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,     _lowerCamelCase    ,     atol=1e-3				)				)
         # verify image_id
         UpperCamelCase_:   List[str]        =							torch.tensor([3_9_7_6_9]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,     _lowerCamelCase				)				)
         # verify is_crowd
         UpperCamelCase_:   List[Any]        =							torch.tensor([0, 0, 0, 0, 0, 0]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,     _lowerCamelCase				)				)
         # verify class_labels
         UpperCamelCase_:   Optional[Any]        =							torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,     _lowerCamelCase				)				)
         # verify masks
         UpperCamelCase_:   Optional[Any]        =							8_2_2_8_7_3
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,     _lowerCamelCase				)
         # verify orig_size
         UpperCamelCase_:   int        =							torch.tensor([4_8_0, 6_4_0]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,     _lowerCamelCase				)				)
         # verify size
         UpperCamelCase_:   str        =							torch.tensor([8_0_0, 1_0_6_6]				)
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,     _lowerCamelCase				)				)



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
						from .pipeline_kandinsky import KandinskyPipeline
						from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
						from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
						from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
						from .text_encoder import MultilingualCLIP


